Jangine is a chess engine (computer program that plays chess) I wrote in 2022 (parts in 2015).
Logo of Jangine, created by DALL-E
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Board representation is done with 10x12 boards.
- This is similar to 8x8 boards but only needs a single index to address each square, instead relying on sentinel values for efficient out-of-bounds detection. This leads to a simple programming model.
- Speed could be improved with the more advanced technique of bitboards which I haven't had time to research yet.
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Evaluation is very simple. It is based entirely on material counting and piece-square tables.
- This means each piece type gets a value based on the square it is on and the game phase (middlegame/endgame).
- The square values were obtained from looking at other engines, my chess player intuition, and testing.
- The piece values were chosen to stop the engine from giving up N+B for R+P or Q for R+R.
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Search: Traditional alpha-beta search with quiescence search for correctness.
- Transposition table of constant size 176 MiB for storing information about searched positions.
- Iterative deepening is used so that the best move from depth-n-search can be stored as hash move in the hash table and tried first during depth-(n+1)-search.
- Null move pruning, delta pruning
- Depth extensions for
- checks
- pawn reaching seventh rank
- In order to generate as many alpha-beta cutoffs as possible, moves are tried in the following order:
- Hash move
- Captures in MVV-LVA order
- Killer moves (killer heuristic)
- Other quiet moves (incl. promotions) in no particular order
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For now: No principal variation search (did not speed program up), no late move reductions (risky!), no futility pruning (I cannot detect checks efficiently), no static exchange evaluation.
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Opening/endgame optimizations:
- No opening or endgame database is built-in, and should be provided externally.
- No specialized strategies either, so even simple endgame plans might not be found.
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Time management:
- Iterative deepening, keep deepening while trying to use under 5% of the remaining time.
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Language choice:
- C (later C++) was chosen in 2015 due to superior speed over most languages.
- Because I had no C/C++ experience in 2015 (and still don't have very much), there are code quality issues.
- Since many features (e.g. using a std::map vs a static C array) of C++ are too slow for my use case I am thinking of switching the engine back to C entirely.
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UCI/XBoard support: Partial, both being worked on.
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GUI: I use the Arena Chess GUI for testing.
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Strength: On a (Ryzen 3600X single-core):
- [33d1b644 2022-06-20] Was able to beat my NM friend 6-0 in Blitz (3+0) and 37½-2½ (35W 3D 1L) in Bullet (1+0)
Play me on Lichess! https://lichess.org/@/jangine
Build:
g++ -Ofast jangine.cpp -o jangine
Build on Linux for Windows:
sudo apt install mingw-w64
x86_64-w64-mingw32-g++ -Wall -c -g -Ofast jangine.cpp -o jangine.o
x86_64-w64-mingw32-g++ -static -static-libgcc -static-libstdc++ -o jangine.exe jangine.o
Run the included test suites:
# Run set of demo positions (checkmate, stalemate, promotion, en passant, etc.)
./jangine -t
# Solve tactics exercises from the book "Win At Chess"
python test_win_at_chess.py # Python >= 3.7
Run as an interactive (stdin/stdout) UCI engine:
./jangine
To host bot for lichess, clone the lichess-bot project https://github.com/ShailChoksi/lichess-bot and place the
engine binary in the engines/ folder.
| Version | G1920 2+1 | est. CCRL | G2315 2+1 | est. CCRL | CCRL |
|---|---|---|---|---|---|
| 2022-12-01 | - | - | - | - | - |
| 2022-12-27 | - | - | - | - | - |
| 2022-12-29 | 82.0/110 (74.5%) | 2106 | 18.5/100 (18.5%) | 2057 | 1938* |
| 2023-01-15 | 83.5/ 99 (84.3%) | 2213 | 115.0/500 (23.0%) | 2105 | - |
| 2023-03-31 | - | - | 146.0/500 (29.2%) | 2161 | - |
*: Dubious rating as print statements were leading to large time losses on buggy testing setup
G2315 is a 10-man gauntlet against the following opponents rated an average of 2315:
- (2374) Jazz 721
- (2361) Jackychess-0.13.4.jar
- (2346) Plisk-0.2.7d
- (2343) Chess4j-5.1-linux.jar
- (2324) Mediocre_v0.5.jar
- (2319) AICE Linux 0.99.2
- (2310) Sungorus64-1.4
- (2271) Paladin-0.1
- (2264) Phalanx-scid-3.61
- (2242) SpaceDog-0.97.7-Linux
G1920 is an 11-man gauntlet against the following opponents rated an average of 1920:
- (1995) Gully-2.16pl1
- (1965) Weasel-1.0.2-Beta
- (1965) Sissa-64-2.0
- (1962) ALChess_184_x64
- (1954) Tinman-v0.4.0
- (1927) Heracles 0.6.16
- (1912) Matmoi-7.15.0-cct
- (1876) Rustic-alpha-3.0.0-linux-64-bit-popcnt
- (1863) Deepov-0.4
- (1856) Fatalii-v0.3.1-x86_64-unknown-linux-musl
- (1842) Sayuri-2018.05.23-linux-64bit
CCRL rating estimated by assuming opponents in a group are rated close to the average:
1 / (1 + 10^((opponentAverage - myRating)/400)) = scorePercentage
Rearranges to:
myRating = opponentAverage - 400 * math.log10(1/scorePercentage - 1)